This project was making new functions for Formstack, a website empowers teams of all sizes to capture data and to perform research through the creation of surveys. It had already been strong in collecting datas but lack of powerful tool analyzing them. Therefore we were trying to make it easier for their customers getting to useful
Problem solvers who want to find out the
solution and reasons.
What did we learn about problem solving?
By reading many articles and interviewed lots of problem solvers around, we found two conclusive quote:
All of the reasons must come from observations and experiments
- Galileo Galilei
.....................hypothesis testing is one of the most important concepts... [if] you want to prove that your data is statistically significant and unlikely to have occurred by chance alone. In essence then,a hypothesis test is a test of significance.”
- San Jose State University Statistics Department
People need hypotheses for surveys if they are looking for reasons
● Since reasons are supporting the solutions, reasons are
necessary to problem solvers
● Since reasons must come from observation and experiments, then making a hypothesis is the first step for any experiment
People find the noteworthy points by comparing against
● By comparing with the hypothesis, people notice the significance from discrepancies. And those reveal the problems.
A business manager of a department of 100 employees.
There was a pervasive negative attitude within the company last quarter, so he wanted to know more about the satisfaction of his employees.
Due to his experience and previous research, he supposed the reason was long working periods every day, and he needs to do deeper research about this issue.
Let's see how he get the reasons by Formstack Expectation.
Start the Survey
This is the main page of setting up a survey, there are various type of questions to be selected on the left. The mainbody of the survey is on the right.
When choosing a certain question, and click the Expectation button above, the left bar turns into expectation settings. For this example, Jack is caring abou the proportion of each option, so the initial numbers are equally devided into 20% for each. And they are shown by a pie chart.
Now Jack wants to set up a certain expected proportion, and he just need to type in the number, either in the survey question bar or in the left bar. The clockwise neighboring category will adjust to his input automatically.
Or he can just drag the line between two pies of the chart to adjust the number. If he wants to prevent from changing the neignboring category, he can lock it, and the clockwisely next category will adjust to the change instead.
Gray arrows in the prototype are indicating the mouse and line movements.
Now Jack has got enough data for his survey, and he is at the analyzing page with all data visualized. We are using the bar chart as an example here.
By clicking the Expectation pull down menu on the right, and choose show discrepancy, Jack can see the difference between obtained data and his expectation. Which will be presented at next page.
The blue bars indicate the actual values exceed the expected values. They look like being penatrated by the thinner bars. The left side of blue bars are the expected value.
The red bars indicate the actual values are less than expected. The right side of red bars are expected values.
By the color, it is intrinsically to observe the discrepancy between reality and expectation.
Also Jack can choose other tye of charts to observe. For line charts, the expected values are all represented by gray dots.
By the comparing the tendency of these lines, it is also intrinsically to observe the discrepancy.